models = [(name, score) for name, score in models if name != 'Perceptron']

#In Modellen(name, score)Der Name des Teils von
#'Perceptron'Es bedeutet, dass nur derjenige übrig bleibt, der nicht ist.

[('KNeighbors',
  KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
             metric_params=None, n_jobs=1, n_neighbors=5, p=2,
             weights='uniform')),
 ('DTC',
  DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
              max_features=None, max_leaf_nodes=None,
              min_impurity_split=1e-07, min_samples_leaf=1,
              min_samples_split=2, min_weight_fraction_leaf=0.0,
              presort=False, random_state=None, splitter='best')),
 ('SVM', SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,
    decision_function_shape=None, degree=3, gamma='auto', kernel='rbf',
    max_iter=-1, probability=False, random_state=None, shrinking=True,
    tol=0.001, verbose=False)),
 ('LinearSVC',
  LinearSVC(C=1.0, class_weight=None, dual=True, fit_intercept=True,
       intercept_scaling=1, loss='squared_hinge', max_iter=1000,
       multi_class='ovr', penalty='l2', random_state=None, tol=0.0001,
       verbose=0))]

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